Skip to main content
Glama
JLKmach

ServiceNow MCP Server

by JLKmach

update_changeset

Modify an existing ServiceNow changeset by updating its name, description, state, or assigned developer to reflect current project requirements.

Instructions

Update an existing changeset in ServiceNow

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
changeset_idYesChangeset ID or sys_id
nameNoName of the changeset
descriptionNoDescription of the changeset
stateNoState of the changeset
developerNoDeveloper responsible for the changeset

Implementation Reference

  • The main handler function that executes the update_changeset tool. It validates parameters using UpdateChangesetParams, prepares a PATCH request to the ServiceNow sys_update_set table, and returns the updated changeset or error.
    def update_changeset(
        auth_manager: AuthManager,
        server_config: ServerConfig,
        params: Union[Dict[str, Any], UpdateChangesetParams],
    ) -> Dict[str, Any]:
        """
        Update an existing changeset in ServiceNow.
    
        Args:
            auth_manager: The authentication manager.
            server_config: The server configuration.
            params: The parameters for updating a changeset. Can be a dictionary or a UpdateChangesetParams object.
    
        Returns:
            The updated changeset.
        """
        # Unwrap and validate parameters
        result = _unwrap_and_validate_params(
            params, 
            UpdateChangesetParams, 
            required_fields=["changeset_id"]
        )
        
        if not result["success"]:
            return result
        
        validated_params = result["params"]
        
        # Prepare the request data
        data = {}
        
        # Add optional fields if provided
        if validated_params.name:
            data["name"] = validated_params.name
        if validated_params.description:
            data["description"] = validated_params.description
        if validated_params.state:
            data["state"] = validated_params.state
        if validated_params.developer:
            data["developer"] = validated_params.developer
        
        # If no fields to update, return error
        if not data:
            return {
                "success": False,
                "message": "No fields to update",
            }
        
        # Get the instance URL
        instance_url = _get_instance_url(auth_manager, server_config)
        if not instance_url:
            return {
                "success": False,
                "message": "Cannot find instance_url in either server_config or auth_manager",
            }
        
        # Get the headers
        headers = _get_headers(auth_manager, server_config)
        if not headers:
            return {
                "success": False,
                "message": "Cannot find get_headers method in either auth_manager or server_config",
            }
        
        # Add Content-Type header
        headers["Content-Type"] = "application/json"
        
        # Make the API request
        url = f"{instance_url}/api/now/table/sys_update_set/{validated_params.changeset_id}"
        
        try:
            response = requests.patch(url, json=data, headers=headers)
            response.raise_for_status()
            
            result = response.json()
            
            return {
                "success": True,
                "message": "Changeset updated successfully",
                "changeset": result["result"],
            }
        except requests.exceptions.RequestException as e:
            logger.error(f"Error updating changeset: {e}")
            return {
                "success": False,
                "message": f"Error updating changeset: {str(e)}",
            }
  • Pydantic BaseModel defining the input schema for the update_changeset tool, including required changeset_id and optional fields for update.
    class UpdateChangesetParams(BaseModel):
        """Parameters for updating a changeset."""
    
        changeset_id: str = Field(..., description="Changeset ID or sys_id")
        name: Optional[str] = Field(None, description="Name of the changeset")
        description: Optional[str] = Field(None, description="Description of the changeset")
        state: Optional[str] = Field(None, description="State of the changeset")
        developer: Optional[str] = Field(None, description="Developer responsible for the changeset")
  • MCP tool registration in get_tool_definitions() dictionary, associating 'update_changeset' name with its handler (aliased import), input schema, return type hint, description, and serialization method.
    "update_changeset": (
        update_changeset_tool,
        UpdateChangesetParams,
        str,  # Expects JSON string
        "Update an existing changeset in ServiceNow",
        "json_dict",  # Tool returns Pydantic model
    ),
  • Export of the update_changeset handler function from the tools package __init__ for easy import.
    update_changeset,
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Update' implies a mutation, it doesn't specify required permissions, whether the update is reversible, what happens to unspecified fields, or any rate limits. This leaves significant gaps for a mutation tool with zero annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without any fluff. It's appropriately sized and front-loaded, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with no annotations and no output schema, the description is inadequate. It lacks details on behavioral aspects like permissions, side effects, or response format, and doesn't differentiate from similar update tools in the context. Given the complexity of updating a changeset, more context is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, with each parameter well-documented in the input schema. The description adds no additional parameter information beyond what's already in the schema, so it meets the baseline of 3 where the schema does the heavy lifting without compensating for any gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Update') and resource ('an existing changeset in ServiceNow'), making the purpose immediately understandable. However, it doesn't differentiate this tool from similar update tools like 'update_change_request' or 'update_workflow' among the siblings, which would require mentioning what specifically distinguishes a changeset update.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With siblings like 'update_change_request' and 'update_workflow', there's no indication of the differences between updating a changeset versus other ServiceNow entities, nor any prerequisites or context for usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/JLKmach/servicenow-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server